Please use this identifier to cite or link to this item:
|Title:||Composite reliability evaluation using Monte Carlo simulation and least squares support vector classifier||Authors:||Pindoriya, N.M.
|Keywords:||Composite power system reliability evaluation
least squares support vector classifier
Monte Carlo simulation
|Issue Date:||Nov-2011||Citation:||Pindoriya, N.M., Jirutitijaroen, P., Srinivasan, D., Singh, C. (2011-11). Composite reliability evaluation using Monte Carlo simulation and least squares support vector classifier. IEEE Transactions on Power Systems 26 (4) : 2483-2490. ScholarBank@NUS Repository. https://doi.org/10.1109/TPWRS.2011.2116048||Abstract:||This paper presents a fast and efficient method which combines the Monte Carlo simulation (MCS) and the least squares support vector machine (LSSVM) classifier, for reliability evaluation of composite power system. LSSVM is used to accurately pre-classify the power system operating states as either success or failure states during the Monte Carlo sampling. These pre-classified failure states are then evaluated for adequacy analysis using DC power flow to calculate reliability indices. As a result, the computing time to perform power flow analysis of the system success states is eliminated. The proposed hybrid method is applied to the IEEE Reliability Test System (IEEE-RTS-79) and simulation results obtained using LSSVM with linear and nonlinear kernels are compared with that of nonsequential MCS. These promising results demonstrate the efficacy of the proposed MCS-LSSVM based hybrid method in terms of both classification accuracy and computational time in evaluating the composite power system reliability. © 2006 IEEE.||Source Title:||IEEE Transactions on Power Systems||URI:||http://scholarbank.nus.edu.sg/handle/10635/55375||ISSN:||08858950||DOI:||10.1109/TPWRS.2011.2116048|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jul 4, 2020
WEB OF SCIENCETM
checked on Jun 26, 2020
checked on Jun 29, 2020
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.